Consumer health information is growing in importance and popularity, with computer networks such as the Internet providing a growing share of the information. It is estimated that health issues are addressed at tens of thousands of online sites with potentially millions of pages of health-related works or content. With a general lack of clinical and editorial standards for health-related content, lay consumers without specific medical training, and even trained medical professionals, can have relatively little success in finding desired or relevant information among such vast resources.
Moreover, given the extremely personal nature of health, most individuals have minimal interest in browsing materials that have no relevance to their health or the health of their families. Yet most of the health information available at conventional network (e.g., Internet) sites or portals addresses only general topics. Such information seldom has any particular relevance to individual users. Accordingly, there is a need for an improved way of obtaining relevant or personalized health-related content from computer networks such as the Internet.
Conventional network (e.g., Internet) systems employ a variety of personalization processes that at least minimally personalize a network site for different visitors or users. The personalization provided by many such processes is relatively simplistic and provides personalization only to the extent of a small number of personalization options. These conventional personalization processes include Greetings, which can be as simple as providing a “welcome sign” that informs the user of the state of a single condition, such as, “Hello you've got mail;” Pick Lists, which allow users to select from predetermined lists of news categories, horoscopes, sports scores, etc.; Keywords, codes or symbols, which can be referenced by entering keywords such as zip codes for local weather forecasts or stock ticker symbols for stock quotes; Demographic/traffic analysis, which is usually derived from a log file which indicates a user's name, email address, zip code, and Internet Service Provider information; Comparison methods, which use data provided by other users to highlight similarities and differences among users; and Collaborative processes, which select content or works based on the preferences of others who are in some way similar to the user.
Personalization processes in use today, including the use of demographics and pick-lists, are inadequate for the vast amounts of health-related information and the relatively narrow interests of many users. Pick Lists are useful, when the possible selections number fewer than several (e.g., 4 or 5) dozens. However, health related content can be usefully categorized among hundreds or thousands of distinct topics. As a consequence, conventional health-related network sites that employ Pick Lists for personalization typically provide relatively few selections that each cover broad areas of information. Such broad coverage areas render such personalization ineffective for the specific health-related information desired by many users.
The present invention provides personalization of access to health-related content on a computer network based upon a health history of a user. In one implementation, personal health-related information about the user is obtained from a user operating a client computer. The health-related information includes one or more health-related terms that each corresponds to a health-related concept. The personal health information may relate to health conditions, which may include medical diagnoses like diabetes, high blood pressure, pneumonia, or pregnancy, or any current or past health problems like poor vision, chronic joint pain, cancer, or alcoholism. The health information could also or alternatively relate to allergies, tests, vaccinations, surgeries or procedures, etc. that affect or have affected the health of the user or that are a part of the user's health history.
The health related terms provided by the user are correlated with a health terminology thesaurus that is stored on a computer-readable medium, such as at a server remote from the user client. Each of the health-related terms is associated with a single concept unique identifier that uniquely identifies a corresponding health-related concept. Each concept unique identifier has associated with it one or more terms corresponding to a common health-related concept. Some of the terms are clinical medical terms and others are lay medical terms that are not clinical medical terms.
Health-related works or content that is accessible over a computer network may be identified in a personalized manner based upon the concept unique identifiers. The health-related content may include, for example, health news, product and service information, disease information, medication information, and other health-related content. Each health-related work has associated with it one or more concept unique identifiers. Personalized identification of the health-related works entails matching the concept unique identifiers of the terms provided by the user with the concept unique identifiers of works relating to those terms. In addition, the Concept Unique Identifier is related to other Concept Unique Identifiers to give it greater semantic meaning and context. The relationships of concepts are derived from existing professional healthcare vocabularies, including Snomed, Medical Subject Headings, and International Classification of Diseases. These relationships allow the term “type 2 sugar disease” which equates to the concept of adult-onset diabetes mellitus, to be related as a narrower concept to diabetes mellitus, which in turn is a narrower concept than diabetes, which in turn is a narrower concept to endocrine and glandular disorders. This then allows an article written simply about “Diabetes” to find all those who would benefit from this information, including those who described themselves as having “type 2 sugar disease.”
The present invention provides personalized access to health-related information that can accommodate the particular interests of both professional and lay users and the vast amounts of and conflicting terminology in health-related information. In contrast, conventional personalization processes are inadequate for the particularized interests of users in combination with the vast and complex resources of health-related information.
Additional objects and advantages of the present invention will be apparent from the detailed description of the preferred embodiment thereof, which proceeds with reference to the accompanying drawings.